Discontinuous Breaks Identification and Space Vibrancy Deduction of Recreational Path Network in Urban Area

doi:10.12141/j.issn.1000-565X.230581

Abstract

Abstract:

Improving the vitality of self-organized recreational path network is an important goal of urban renewal planning. Due to the discontinuity and the existence of dead end roads in the recreational path network in old towns, the comfort of residents’ recreational activities is restricted. The self-organization of recreational path network means that the top-down planning and design mode is difficult to match the needs of citizens. How to deduce the spatial vitality of recreation planning in a bottom-up mode and then identify potential land plots is an important issue to improve the quality of urban recreational path network. By taking the old town in Guangzhou as an example, this paper constructs the indicators of break degree and spatial vibrancy based on the complex network theory. Then, by using GPS trajectory data to identify the breaks in the existing recreational path network, the identification of discontinuous breaks based on citizen recreation behavior and the activation effect of urban renewal projects are quantitatively discussed, and the potential impacts brought by the implementation of renewal projects are simulated. The discontinuity analysis shows that the break degree of the recreational path network in the old town in Guangzhou is generally low, and the connection degree is good, but there are still some obvious breaks due to the street location and historical problems. Moreover, the results of spatial vibrancy deduction show that urban renewal projects have a positive impact on the self-organized recreational path network, and that the implementation plan and schedule of urban recreation quality improvement projects can be determined according to the deduction analysis results. This study makes a beneficial exploration of urban self-organizing characteristics from the perspectives of planning and transportation, and makes up for the deficiency of top-down planning in considering urban self-organized recreational path network.

Key words: recreational path network, urban planning, urban renewal, discontinuity, spatial vibrancy, planning simulation

CLC Number:

TU984.191

ZHAO Miaoxi, LIANG Xiyan, ZHANG Xiaoxing, SHI Haochen. Discontinuous Breaks Identification and Space Vibrancy Deduction of Recreational Path Network in Urban Area[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(7): 88-96.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

References 35

1	COSTA C ．An emerging tourism planning paradigm？A comparative analysis between town and tourism planning［J］．International Journal of Tourism Research，2001，3（6）：425-441．
2	MURAKAMI E， WAGNER D P ．Can using global positioning system （GPS） improve trip reporting？［J］．Transportation Research Part C Emerging Technologies，1999，7（2/3）：149-165．
3	张治华．基于GPS轨迹的出行信息提取研究［D］．上海：华东师范大学，2010．
4	EDWARDS D， GRIFFIN T ．Understanding tourists’spatial behavior：GPS tracking as an aid to sustainable destination management［J］．Journal of Sustainable Tourism，2013，21（4）：580-595．
5	TACZANOWSKA K， GARCIA-MASSÓ X， MUHAR A，et al ．Evaluating the structure and use of hiking trails in recreational areas using a mixed GPS tracking and graph theory approach［J］．Applied Geography，2014，55：184-192．
6	SHEN J， CHENG T ．A framework for identifying acti-vity groups from individual space-time profiles［J］．International Journal of Geographical Information Science，2016，9（30）：1785-1805．
7	NIELSEN N C， HARDER H， TRADISAUSKAS N，et al ．Approaches to GPS-survey of tourist movements within a North Sea island destination［C］∥Proceedings of ENTER 2010 Conference．Lugano：［s. n.］，2010：10-12．
8	ZACHARIAS J， ZHANG R ．Revealed bicyclist route preferences and street conditions［J］．Transportation Research Record Journal of the Transportation Research Board，2016，2587（1）：17-22．
9	GONZÁLEZ F， MELO-RIQUELME C， GRANGE L D ．A combined destination and route choice model for a bicycle sharing system［J］．Transportation，2016，43（3）：407-423．
10	黄潇婷，李玟璇，张海平，等．基于GPS数据的旅游时空行为评价研究［J］．旅游学刊，2016，31（9）：40-49．
	HUANG Xiaoting， LI Wenxuan， ZHANG Haiping，et al ．Evaluation of tourist temporal-spatial behavior based on GPS data［J］．Tourism Tribune，2016，31（9）：40-49．
11	NEWMAN M E J ．Assortative mixing in networks［J］．Physical Review Letters，2002，89（20）：208701/1-5．
12	NARAYANA M V ．Route optimization by using multiple travelling sales person problem in MANETs［J］．International Journal of Scientific Research in Computer Science，Engineering and Information Technology，2018，3（1）：782-790．
13	BAGGIO R， SCOTT N， COOPER C ．Network science：a review focused on tourism［J］．Annals of Tourism Research．2010，37（3）：802-827．
14	刘亮．复杂网络基元研究方法及应用［M］．上海：上海交通大学出版社，2018．
15	BOCCALETTI S， LATORA V， MORENO Y，et al ．Complex networks：structure and dynamics［J］．Phy-sics Reports，2006，424：175-308．
16	HILLIER B， HANSON J ．The social logic of space［M］．Cambridge：Cambridge University Press，1989．
17	GEHL J ．Life between buildings［M］．［S. l.］：VAN Nosrand Reinhold，1987．
18	LIU Z， LIN Y， DE MEULDER B，et al ．Can greenways perform as a new planning strategy in the Pearl River Delta，China？［J］．Landscape and Urban Planning，2019，187：81-95．
19	GOBSTER P H ．Perception and use of a metropolitan greenway system for recreation［J］．Landscape and Urban Planning，1995，33：401-413．
20	何卓书，许欢，黄俊浩．基于空间句法的历史街区商业空间分布研究——以广州长寿路站周边街区为例［J］．南方建筑，2016（5）：84-89．
	HE Zhuoshu， XU Huan， HUANG Junhao ．Commercial distribution research of historic area based on space syntax：a case study of historic area surrounded Changshoulu Station in Guangzhou［J］．South Architecture，2016（5）：84-89．
21	盛强，杨滔，刘宁．空间句法与多源新数据结合的基础研究与项目应用案例［J］．时代建筑，2017（5）：38-43．
	SHENG Qiang， YANG Tao， LIU Ning ．Fundamental research and project applications of integrating space syntax and multiple types of new data［J］．Time+Architecture，2017（5）：38-43．
22	PORTUGALI J ．What makes cities complex？Complexity，cognition，urban planning and design［R］∥Springer Proceedings in Complexity．［S. l.］：Springer，2016．
23	张森，张薇，曾坚．城市绿道公园活跃度的影响因子及其统计［J］．城市问题，2017（4）：98-103．
	ZHANG Sen， ZHANG Wei， ZENG Jian ．Influencing factors and statistics of urban greenway park activity［J］．Urban Problems，2017（4）：98-103．
24	褚冬竹，阳蕊．线·索：重庆城市微更新时空路径与实践特征［J］．建筑学报，2020（10）：58-65．
	CHU Dongzhu， YANG Rui ．Threads and clues： spatio-temporal evolution paths and characteristics of micro regeneration practices in Chongqing［J］．Architectural Journal，2020（10）：58-65．
25	龙瀛，刘伦．新数据环境下定量城市研究的四个变革［J］．国际城市规划，2017，32（1）：64-73．
	LONG Ying， LIU Lun ．Four transformations of Chinese quantitative urban research in the new data environment［J］．Urban Planning International，2017，32（1）：64-73．
26	LI J， XU L， TANG L，et al ．Big data in tourism research：a literature review［J］．Tourism Management，2018，68：301-323．
27	BATTY M ．The pulse of the city［J］．Environment and Planning B：Planning and Design，2010，37（4）：575-577．
28	ZHAO M， HUANG J， LIN Y，et al ．A method for generating urban cognation maps based on Internet words frequency：CN105574259A［P］．2016-05-11．
29	龙瀛，周垠．街道活力的量化评价及影响因素分析——以成都为例［J］．新建筑，2016（1）：52-57．
	LONG Ying， ZHOU Yin ．Quantitative evaluation on street vibrancy and its impact factors：a case study of Chengdu［J］．New Architecture，2016（1）：52-57．
30	IBRAHIM M R， HAWORTH J， CHENG T ．Understanding cities with machine eyes：a review of deep computer vision in urban analytics［J］．Cities，2020，96：1-13．
31	广州市规划和自然资源局．广州市城市更新专项总体规划（2018—2035）［Z］．广州：广州市规划和自然资源局，2019．
32	ZHAO M， LU H， LIANG J，et al ．Evaluating green resource branding using user-generated content data：the case study of a greenway in eastern Guangzhou，China［J］．Urban Forestry and Urban Greening，2021，66：127395．
33	BARRAT A， BARTHÉLEMY M， VESPIGNANI A ．Weighted evolving networks：coupling topology and weight dynamics［J］．Physical Review Letters，2004，92（22）：228701/1-4．
34	赵渺希，朵朵．巨型城市区域的复杂网络特征［J］．华南理工大学学报（自然科学版），2013，41（6）：108-115．
	ZHAO Miao-xi， Duo DUO ．Complex network features of mega city region［J］．Journal of South China University of Technology （Natural Science Edition），2013，41（6）：108-115．
35	KANDT J， BATTY M ．Smart cities，big data and urban policy：towards urban analytics for the long run［J］．Cities，2021，109（2）：102992/1-10．